The Paradox No One Wants to Admit
The consumer goods industry has never had access to this much commercial intelligence. Elasticity models, scenario simulators, price optimization platforms, competitive intelligence dashboards, generative AI tools. And yet most organizations still leave money on the table. The cause is rarely a lack of data. More often, it is a systematic inability to convert their best decisions into consistent commercial action.
Let’s call it what it is: the last mile of Revenue Growth Management.
It is the distance, sometimes enormous, between a pricing recommendation and its actual execution at the point of sale, in the customer negotiation, in the promotional rollout, on the restaurant floor. In our view, it is the largest source of value destruction in consumer organizations today.
The End of an Era: When Price Stopped Being Enough
For the past five years, the growth equation in consumer goods was deceptively simple. Inflation let companies grow through price without needing operational sophistication. According to recent industry analysis, price increases accounted for more than 90% of CPG sales growth since 2019. For many Revenue Growth Management teams, this meant operational gaps stayed hidden beneath nominal growth that looked healthy.
That era is over.
Inflation is normalizing and consumers are growing more price sensitive. Private label already accounts for 42% of CPG sales value in major European markets, and the trend is accelerating across Latin America. The structural gaps that aggressive pricing once masked are now fully exposed. The traditional model of 4% annual growth, built on population growth, real consumption, and pricing, no longer works the way it used to. Companies that fail to solve the last mile of RGM will find their real problem sits in conversion, not strategy.
In our experience working alongside consumer and foodservice organizations across Latin America and Europe, the clearest signal of this shift is what CFOs ask. They no longer ask, “What is our pricing strategy?” Now they ask, “Why isn’t our pricing strategy showing up in the results?”
The Wrong Diagnosis: More Tools, Fewer Results
A dangerously common belief runs through the industry: that the RGM problem is a technology problem. If the elasticity model were more precise, if the platform were faster, if generative AI could simulate scenarios, the logic goes, results would improve.
The data tells a different story. According to the Promotion Optimization Institute, only 10% of CPG organizations have reached prescriptive analytics maturity. But the truly revealing figure is different: 43% of companies still use Excel as their primary tool for price architecture decisions, and 47% still run promotional plans off the prior year’s calendar.
This is not a tooling gap. Tools and decision processes simply operate in parallel universes, rarely intersecting where a real decision gets made.
In most of the organizations we diagnose, we find pricing and analytics teams producing solid recommendations. But those recommendations travel a tortuous organizational path. They pass through layers of commercial leadership review, get modified in customer negotiations, get adapted “on the fly” by field teams, and arrive at the point of execution, when they arrive at all, carrying a significant deviation from the original intent.
In our view, the execution gap is an organizational design problem, not a technical one.
The Concept of “Decision Adoption”: What Really Separates Leading Companies
Across the most advanced RGM forums in 2026, one concept has emerged with force: decision adoption. Generating the best recommendation is not enough. What determines the outcome is whether that recommendation gets adopted, executed, and sustained over time.
This marks a fundamental shift. RGM teams traditionally measured success by the quality of their analysis. The companies pulling ahead of the pack now measure success by the adoption rate of their recommendations on the commercial front line.
What truly sets successful companies apart is that they have built what we call a commercial conversion system. It is an integrated mechanism in which insight generation, decision making, financial validation, and execution operate as a single continuous process rather than a sequence of disconnected stages.
Across the projects we have run, we have identified five recurring failure points that destroy the last mile:
- Timing misalignment. Pricing recommendations arrive too late in the negotiation cycle. A brilliant analysis that lands a week after the commercial team has already closed terms with a distributor has zero value.
- The disconnect between dashboards and workflows. Organizations invest in sophisticated visualization platforms that nobody consults at the moment the real decision gets made. The dashboard lives in one world. The commercial negotiation lives in another.
- The absence of executable pricing governance. Many companies have pricing policies. Few have governance: clear mechanisms that determine who can modify a price, under what conditions, with what approvals, and with what consequences when someone deviates from the plan.
- Fragmented ownership. 50% of organizations operate with a fragmented pricing ownership model. Pricing sets the strategy, commercial executes it or modifies it, finance evaluates it after the fact, and no one owns the end-to-end result.
- Missing feedback loops. Recommendations go out, but no one tracks their adoption or their impact. Without systematic feedback, there is no organizational learning, and the same mistakes repeat quarter after quarter.
The Lesson Coming From Foodservice: The Profitability Gap Is Already Opening
What is happening in the restaurant and foodservice sector is a case study that should alarm, or inspire, the entire consumer industry.
Recent research shows that 62% of restaurant operators have already implemented or plan to implement AI in at least one back-office function, more than double the level reported in early 2026. The results for adopters are concrete: 61% report food cost reductions, 62% report labor cost reductions, and 88% report meaningful weekly time savings. Nearly a third report cost reductions above 6%.
But the real story goes beyond these individual numbers. A structural profitability gap is opening between operators who embed analytical intelligence into their daily operating decisions and those still running on intuition and spreadsheets.
In our experience with restaurant chains and foodservice operators, the difference between those who capture value and those who do not rarely comes down to the sophistication of their technology. It comes down to the ability to connect three elements that most treat as silos: menu analysis (pricing), inventory management (cost), and team operations (execution).
A restaurant operator that optimizes menu prices but lacks real-time visibility into the true cost of each dish is flying blind. And one that has both but whose kitchen team is not aligned with margin priorities is executing a plan that exists only on a screen.
The lesson for the consumer goods industry is direct: technology only creates value when it operates inside a system that connects strategy, operations, and execution in a continuous cycle. Outside that system, it is just another investment without a return.
The Last-Mile Framework: How to Build a Commercial Conversion System
The question executives who recognize this problem ask us most often is a practical one: how do you close the gap?
After working with organizations of different sizes and maturity levels, we have developed an approach that organizes the solution into four layers. It is a transformation of the organization’s commercial operating system, not a technology rollout.
Layer 1: Embedding the decision in the workflow. Pricing, promotion, and mix recommendations must reach the decision maker at the exact moment the decision happens, not before and not after. This means redesigning commercial processes so analytical intelligence becomes a native input rather than an optional report.
Layer 2: Governance with teeth. Define clear rules of authority over prices: who can modify them, within what bands, with what escalation path. More important still, measure and publish the adherence rate. Organizations that share internally what percentage of pricing recommendations were executed exactly as designed see a dramatic increase in commercial discipline.
Layer 3: Integrated ownership of the outcome. Retire the model where pricing sets strategy, commercial executes, and finance evaluates in separate compartments. The best-performing RGM companies are creating roles or committees accountable for the full outcome, from the recommendation through to the P&L impact, with incentives aligned across the entire chain.
Layer 4: Operational feedback cycles. Establish weekly, not monthly or quarterly, reviews that compare the intent of each pricing or promotional decision against its actual outcome. These reviews exist to build organizational learning that steadily improves recommendation accuracy and execution consistency, not to assign blame.
What really matters in this framework is whether all four layers operate as one system, not the sophistication of any single layer in isolation. We have seen organizations implement flawless governance without feedback loops, and end up with rigid rules that never learn. We have seen others with best-in-class analytics but no integration into the workflow, generating reports nobody uses.
The Role of AI: Enabler, Not Solution
No conversation about the last mile of RGM in 2026 is complete without addressing the role of artificial intelligence. But that conversation needs to happen honestly.
According to recent data, only 3% of RGM professionals in consumer goods have fully integrated AI into their processes. 44% remain in pilot phase. This should not surprise anyone who has led an RGM transformation: technology is the easy part. Changing how an organization makes and executes commercial decisions is the hard part.
In our view, AI has three legitimate roles in the last mile, and none of them is replacing human judgment:
The first is compressing the time between insight and action. Prescriptive AI can identify opportunities and generate recommendations in real time, eliminating the timing misalignment that kills many good decisions.
The second is empowering the front line with contextual intelligence. Instead of centralizing all pricing intelligence in a corporate team, AI tools can place tailored recommendations directly in the hands of the salesperson or restaurant manager at the moment of decision.
The third is closing the feedback loop automatically. The ability to monitor price guidance adherence and the impact of every commercial decision in real time turns feedback from a quarterly exercise into a continuous process.
But in every case, AI works inside a system. Organizations that install AI on top of broken processes predictably get broken processes, only faster.
What Should Change Tomorrow
If a consumer goods or foodservice executive finished this article and had to make one decision tomorrow, our recommendation would be this: measure the adoption rate of your pricing recommendations.
Not the quality of your models. Not the speed of your platform. Not the volume of data you process.
Measure what percentage of the pricing decisions your RGM or analytics team recommends actually gets executed in the market, without unauthorized changes, within the intended timeframe. That number, which most organizations do not know, is the most honest indicator of how healthy your Revenue Growth Management system really is.
If the adoption rate is high, the opportunity lies in sophisticating the recommendations further. If the rate is low, as it is in the vast majority of the cases we diagnose, investing in better models before fixing the last mile is like buying a more precise GPS for a car with no engine.
Final Reflection
Real competitive advantage in Revenue Growth Management no longer comes from having better information. Practically every company past a certain size has access to the same market data, the same analytical tools, the same conceptual frameworks. The difference has shifted to less glamorous but far more valuable ground: the ability to convert that information into consistent commercial action, day after day, at every decision point in the organization.
The companies that close the last-mile gap will not necessarily be the ones with the best algorithms or the biggest technology budgets. They will be the ones that treat RGM as a commercial operating system, not an analytical function. For them, the biggest EBITDA opportunity lies in building an organization capable of executing the right price every day, well beyond the challenge of finding it in the first place.
Performa Advisors is a consulting firm specializing in Revenue Growth Management, Pricing, Analytics, and applied Artificial Intelligence, with a focus on consumer goods, food, and restaurant industries.